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https://github.com/deepseek-ai/DeepInfer.git
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d8f7a2e898
- serve/: the minimal OpenAI-compatible server of deepseek-ai/DeepInfer#147, with a Neuron worker setup (basic mode env, per-rank NeuronCore, fixed KV pool via --num-kv-blocks, no MTP lookahead) and a /v1/completions endpoint (prompt text or token ids, no chat template): what vllm bench serve --backend openai drives, as vllm-neuron#71's run_bench.py does. - examples/neuron/bench_neuron.py: the same TTFT/TPOT matrix offline. - static MoE: one packed fp32 all-gather instead of three (each 64-rank collective is ~2 ms of fixed latency a layer); a capacity-grouped expert path for prefill chunks (DEEPINFER_NEURON_MOE_CAPACITY, dense rerun on overflow; not yet validated on device). - Sub-region timings at split() boundaries under DEEPINFER_NEURON_REGION_TIMING. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
88 lines
3.6 KiB
Python
88 lines
3.6 KiB
Python
"""A/B check of the host prefix cache on a running serve/openai_server.py.
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Builds a multi-turn conversation with a long shared prefix, sends every turn twice (cache bypassed via
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"prefix_cache": false, then with the cache) with greedy sampling, and compares outputs and TTFT.
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python serve/check_prefix_cache.py --port 8888 --turns 3 --prefix-tokens 6000
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"""
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from __future__ import annotations
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import argparse
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import json
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import time
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import requests
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p = argparse.ArgumentParser()
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p.add_argument("--port", type=int, default=8888)
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p.add_argument("--model", default="deepseek-ai/DeepSeek-V4.1-Flash-1017")
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p.add_argument("--turns", type=int, default=3)
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p.add_argument("--prefix-tokens", type=int, default=6000, help="approximate size of the shared document")
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p.add_argument("--max-tokens", type=int, default=64)
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args = p.parse_args()
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URL = f"http://localhost:{args.port}/v1/chat/completions"
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# A deterministic pseudo-document; ~1 token per word for this tokenizer is close enough.
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words = ["alpha", "beta", "gamma", "delta", "epsilon", "zeta", "eta", "theta", "iota", "kappa", "lambda", "mu"]
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doc = " ".join(f"{words[i % len(words)]}{i % 97}" for i in range(args.prefix_tokens))
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questions = [
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"Which word appears right after 'gamma2'? Answer briefly.",
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"How many distinct word stems are used in the document? Answer briefly.",
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"Quote the first five words of the document.",
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"What is the last word of the document?",
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]
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def ask(messages: list[dict], use_cache: bool) -> tuple[str, float, float, dict]:
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body = {
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"model": args.model,
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"messages": messages,
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"stream": True,
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"stream_options": {"include_usage": True},
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"temperature": 0,
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"max_tokens": args.max_tokens,
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"seed": 1,
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"prefix_cache": use_cache,
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}
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t0 = time.perf_counter()
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ttft = None
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text, usage = [], {}
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with requests.post(URL, json=body, stream=True, timeout=1800) as r:
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r.raise_for_status()
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for line in r.iter_lines():
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if not line or not line.startswith(b"data: ") or line == b"data: [DONE]":
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continue
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obj = json.loads(line[6:])
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if obj.get("usage"):
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usage = obj["usage"]
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for ch in obj.get("choices", []):
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delta = ch["delta"].get("content")
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if delta:
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if ttft is None:
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ttft = time.perf_counter() - t0
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text.append(delta)
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return "".join(text), ttft or 0.0, time.perf_counter() - t0, usage
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messages = [{"role": "system", "content": "You answer questions about the document below.\n\n" + doc}]
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mismatches = 0
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for turn in range(args.turns):
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messages.append({"role": "user", "content": questions[turn % len(questions)]})
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base, ttft_b, tot_b, usage_b = ask(messages, use_cache=False)
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cached, ttft_c, tot_c, usage_c = ask(messages, use_cache=True)
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same = base == cached
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mismatches += not same
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print(
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f"turn {turn + 1}: prompt {usage_c.get('prompt_tokens')} tok | TTFT no-cache {ttft_b * 1e3:7.0f} ms, cache {ttft_c * 1e3:7.0f} ms "
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f"({ttft_b / max(ttft_c, 1e-6):.1f}x) | outputs {'identical' if same else 'DIFFER'}"
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)
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if not same:
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print(" no-cache:", repr(base[:200]))
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print(" cache :", repr(cached[:200]))
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messages.append({"role": "assistant", "content": base})
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stats = requests.get(f"http://localhost:{args.port}/deepinfer/metrics", timeout=30).text
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print("\n".join(line for line in stats.splitlines() if "hicache:hits" in line or "hicache:lookups" in line or "hit_rate" in line))
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raise SystemExit(1 if mismatches else 0)
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